Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)...Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.展开更多
For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is th...For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is the target.However,uncertainties from load forecasting errors and transmission contingencies are threats to medium-and Iong-term electricity tradi ng in terms of their in flue nces on the GFR.In this paper,we prese nt a graphic distortio n pattern in a typical threegenerator system using the Monte Carlo method and projection theory based on security constrained economic dispatch.The underlying potential risk to GFR from uncertainties is clearly visualized,and their impact characteristics are discussed.A case study on detailed GFR distortion was included to dem on strate the effectiveness of this visualization model.The result implies that a small uncertainty could distort the GFR to a remarkable extent and that different line-contingency precipitates disparate the GFR distortion patterns,thereby eliciting great emphasis on load forecasting and line reliability in electricity transacti ons.展开更多
The load growth is the most important uncertainties in power system planning process. The applications of the classical long-term load forecasting methods particularly applied to utilities in transition economy are in...The load growth is the most important uncertainties in power system planning process. The applications of the classical long-term load forecasting methods particularly applied to utilities in transition economy are insufficient and may produce incorrect decisions in power system planning process. This paper discusses using the method of analytic hierarchy process to calculate the probability distribution of load growth obtained previously by standard load forecasting methods.展开更多
This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage(LV) distribution network for voltage management,energy arbit...This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage(LV) distribution network for voltage management,energy arbitrage or peak load reduction. The methods compared include: a neural network(NN) based prediction scheme that utilizes the load history and the current metrological conditions; a wavelet neural network(WNN)model which aims to separate the low and high frequency components of the consumer load and an artificial neural network and fuzzy inference system(ANFIS) approach.The batteries have limited capacity and have a significant operational cost. The load forecasts are used within a receding horizon optimization system that determines the state of charge(SOC) profile for a battery that minimizes a cost function based on energy supply and battery wear costs. Within the optimization system, the SOC daily profile is represented by a compact vector of Fourier series coefficients. The study is based upon data recorded within the Perth Solar City high penetration photovoltaic(PV)field trials. The trial studied 77 consumers with 29 rooftop solar systems that were connected in one LV network. Data were available from consumer smart meters and a data logger connected to the LV network supply transformer.展开更多
提出了考虑多重不确定性的光伏支撑体系(Photovoltaic Support System,PSS)随机动力可靠性分析方法。首先,构建了基于概率密度演化理论(Probability Density Evolution Method,PDEM)的光伏支撑体系可靠性分析模型,包括概率守恒方程、基...提出了考虑多重不确定性的光伏支撑体系(Photovoltaic Support System,PSS)随机动力可靠性分析方法。首先,构建了基于概率密度演化理论(Probability Density Evolution Method,PDEM)的光伏支撑体系可靠性分析模型,包括概率守恒方程、基本控制方程和密度演化方程;然后,建立了光伏支撑体系的有限元分析模型,包括结构受力模型、荷载组合形式、网格划分算法等。仿真模型中考虑了结构所受荷载与结构本身的随机性,共计6个随机变量和44个代表点。为提升算法分析效率,提出了Abaqus⁃PDEM的联合仿真算法,仿真分析表明,光伏支撑体系的失效模式主要为应力控制和位移控制两种,后者影响更为明显,基本荷载组合工况下的可靠度为0.928。随着风力等级的提高,结构可靠性逐渐降低,在高风速区间(大于40 m/s),结构本身的不确定性会高估结构的可靠性水平,在设计中应予以关注。展开更多
基金the Ministry of Higher Education Malaysia,under the Fundamental Research Grant Scheme(FRGS Grant No.FRGS/1/2016/TK07/SEGI/02/1).
文摘Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.
基金the National Key R&D Program of China under Grant No.2020YFB0905900in part by the State Grid Corporation of China project“Research on inter-provincial price coupling mechanism of national unified electricity spot market”.
文摘For the implementation of power market in China,medium-and Iong-term security checks are essential for bilateral transactions,of which the electricity quantity that constitutes the generation feasible region(GFR)is the target.However,uncertainties from load forecasting errors and transmission contingencies are threats to medium-and Iong-term electricity tradi ng in terms of their in flue nces on the GFR.In this paper,we prese nt a graphic distortio n pattern in a typical threegenerator system using the Monte Carlo method and projection theory based on security constrained economic dispatch.The underlying potential risk to GFR from uncertainties is clearly visualized,and their impact characteristics are discussed.A case study on detailed GFR distortion was included to dem on strate the effectiveness of this visualization model.The result implies that a small uncertainty could distort the GFR to a remarkable extent and that different line-contingency precipitates disparate the GFR distortion patterns,thereby eliciting great emphasis on load forecasting and line reliability in electricity transacti ons.
文摘The load growth is the most important uncertainties in power system planning process. The applications of the classical long-term load forecasting methods particularly applied to utilities in transition economy are insufficient and may produce incorrect decisions in power system planning process. This paper discusses using the method of analytic hierarchy process to calculate the probability distribution of load growth obtained previously by standard load forecasting methods.
文摘This paper compares three methods of load forecasting for the optimum management of community battery storages. These are distributed within the low voltage(LV) distribution network for voltage management,energy arbitrage or peak load reduction. The methods compared include: a neural network(NN) based prediction scheme that utilizes the load history and the current metrological conditions; a wavelet neural network(WNN)model which aims to separate the low and high frequency components of the consumer load and an artificial neural network and fuzzy inference system(ANFIS) approach.The batteries have limited capacity and have a significant operational cost. The load forecasts are used within a receding horizon optimization system that determines the state of charge(SOC) profile for a battery that minimizes a cost function based on energy supply and battery wear costs. Within the optimization system, the SOC daily profile is represented by a compact vector of Fourier series coefficients. The study is based upon data recorded within the Perth Solar City high penetration photovoltaic(PV)field trials. The trial studied 77 consumers with 29 rooftop solar systems that were connected in one LV network. Data were available from consumer smart meters and a data logger connected to the LV network supply transformer.
文摘提出了考虑多重不确定性的光伏支撑体系(Photovoltaic Support System,PSS)随机动力可靠性分析方法。首先,构建了基于概率密度演化理论(Probability Density Evolution Method,PDEM)的光伏支撑体系可靠性分析模型,包括概率守恒方程、基本控制方程和密度演化方程;然后,建立了光伏支撑体系的有限元分析模型,包括结构受力模型、荷载组合形式、网格划分算法等。仿真模型中考虑了结构所受荷载与结构本身的随机性,共计6个随机变量和44个代表点。为提升算法分析效率,提出了Abaqus⁃PDEM的联合仿真算法,仿真分析表明,光伏支撑体系的失效模式主要为应力控制和位移控制两种,后者影响更为明显,基本荷载组合工况下的可靠度为0.928。随着风力等级的提高,结构可靠性逐渐降低,在高风速区间(大于40 m/s),结构本身的不确定性会高估结构的可靠性水平,在设计中应予以关注。
